MNEMO (Methodology for Knowledge Acquisition and Modelling): Definition of a Global Knowledge Management Approach Combining Knowledge Modelling Techniques *1 Maria Teresa Guaglianone, 2 Nada Matta 1 First Author University of Calabria, Rende (Cs), Italy, mariateresa.guaglianone@unical.it 2 University of Technology of Troyes, Troyes Cedex, France, nada.matta@utt.fr Abstract It is widely believed that the most valuable knowledge is also tacit. It is difficult to be preserved, to be made reusable and to be enhanced, because it lies in people's mind and it is hard to express and share. Knowledge engineering techniques have been adapted and used for knowledge management, especially, for the acquisition and modelling of this kind of knowledge. In this paper, we present MNEMO (Methodology for kNowledgE acquisition and MOdelling), a mixed methodological approach, which combines the strengths of two of the mentioned techniques, CommonKADS and MASK (Method of Analysis and Structuring Knowledge), both analyzed and evaluated through real applications in specific domains. The goal is to dispose of a global method, which aims to be complete and flexible in supporting the whole knowledge management cycle, from its acquisition to its exploitation and valorisation, and which can face the constant knowledge memory updating. This predisposition needs to be verified through a testing phase, which will be carried out within the field of building. Keywords: Knowledge Acquisition and Modelling, Knowledge Management, Methodology Comparison, Mnemo 1. Introduction Knowledge is considered a complex but strategic resource, which needs to be identified, expressed, acquired, modelled, stored, diffused and valorized [1]. As it plays a fundamental role in success dynamics, it requires to be managed in order to face possible internal changes of the enterprise, learning or problem solving issues, and to facilitate innovation process [2]. Our interest is in the tacit knowledge, which deals with subconscious ideas, intellectual or physics automatisms which are implicit and difficult to express and articulate. Especially, we focus on tacit knowledge involved in the human expertise and activity, which needs to be made explicit, in order to be shared and reused. According to the classification proposed in [3], which categorize methodologies also on the basis of the kind of knowledge they intend to capture and model, we focused on those concerning human activity and expertise acquisition and modelling. Moreover, this study regards the specific part of externalization in Nonaka and Takeuchi’s theory of organizational knowledge creation. The theory explains how tacit and explicit knowledge interact and how the process of externalization is the conversion from tacit to explicit knowledge. As a matter of fact, knowledge capitalization is significant when it has the knowledge valorization through the reuse as objective. Several methodologies and tools have been developed to meet this aim. Most of these methods, inherited from knowledge engineering and based on knowledge modelling, can be adapted to answer to the aims of knowledge management, which concerns information storage and communication, but also the way in which people create, acquire and use knowledge. They provide relevant techniques and tools, useful for knowledge management, whose principles coincide with those of knowledge engineering. Knowledge engineering methodologies are appropriate to acquire and model implicit knowledge. However, as some of them have been defined to develop knowledge-based system (KBS) and expert system, they need to be adapted to the objectives of knowledge management, which aims to build organizational memories in an easy, rapid and inexpensive way [4]. This need of adaptation makes real and tangible the lack of a complete and adaptable methodology, suitable for the whole knowledge  Correspondig author MNEMO (Methodology for Knowledge Acquisition and Modelling): Definition of a Global Knowledge Management Approach Combining Knowledge Modelling Techniques Maria Teresa Guaglianone, Nada Matta Advances in information Sciences and Service Sciences(AISS) Volume4, Number12, July 2012 doi: 10.4156/AISS.vol4.issue12.19 160